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Educational and Psychological Measurement
DOI: 10.1177/0013164403251335 2004; 64; 290 Educational and Psychological Measurement
Martin Dowson and Dennis M. McInerney The Development and Validation of the Goal Orientation and Learning Strategies Survey (Goals-S)
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10.1177/0013164403251335ARTICLEEDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT
DOWSON AND MCINERNEY
THE DEVELOPMENT AND VALIDATION OF THE GOALORIENTATION AND LEARNING STRATEGIES SURVEY (GOALS-S)
MARTIN DOWSONInstitute of Christian Tertiary Education
DENNIS M. MCINERNEYUniversity of Western Sydney
This article outlines the construction and validation of the Goal Orientation and LearningStrategies Survey (GOALS-S). This 84-item survey was designed to measure students’motivational goal orientations and their cognitive and metacognitive strategies. Resultsof first-order confirmatory factor analyses (CFAs) supported the factorial validity of theGOALS-S scales measuring students’ goals and strategies (with goodness-of-fit indicesin post-hoc models ranging from .908 to .981). In addition, higher order CFAs (HCFAs)support hierarchical structure of the GOALS-S scales (with goodness-of-fit indices rang-ing from .904 to .980). Finally, tests of invariance supported the factorial stability of theGOALS-S scales across gender groups (with goodness-of-fit indices ranging from .901to .981).
Keywords: goal orientations; cognitive strategies; metacognitive strategies; confirma-tory factor analysis
The purpose of the present research was to determine the reliability andvalidity of a new psychometric instrument developed to measure middle andsenior school students’ multiple achievement goals and their cognitive andmetacognitive strategies. Such research is warranted for several reasons.First, students’ (a) academic achievement goals (Ames, 1992; Harackiewicz& Sansone, 1991; McInerney, Hinkley, Dowson, & Van Etten, 1998; Meece,
Correspondence concerning this article should be sent to Martin Dowson, Principal, Instituteof Christian Tertiary Education Ltd., P.O. Box 528, Round Corner, NSW 2158, Australia; e-mail:[email protected].
Educational and Psychological Measurement, Vol. 64 No. 2, April 2004 290-310DOI: 10.1177/0013164403251335© 2004 Sage Publications
290
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1994; Pintrich, Marx, & Boyle, 1993; Urdan & Maehr, 1995), (b) cognitivestrategies (Bergin, 1998; Chamot & El-Dinary, 1996; Garcia & Pintrich,1994; Montague, Applegate, & Marquard, 1993; Reid, Hresko, & Swanson,1991), and (c) metacognitive processes and strategies (Derry, 1990; Graham& Harris, 1992; Paris & Winograd, 1990; Pintrich & Schrauben, 1992; Sink,Barnett, & Hixon, 1991; Zimmerman, 1989) have been shown to profoundlyinfluence the quantity and quality of their engagement in learning(McCombs & Marzarno, 1990; Pervin, 1991; Ridley, 1991; Zimmerman,1990; Zimmerman, Bandura, & Martinez-Pons, 1992). Hence, the accuratemeasurement of these attributes is of interest to educational psychologistsand teaching practitioners.
Second, recent research and theory has suggested that a range of achieve-ment goals, other than those typically measured by existing instruments, mayalso affect students’ engagement in, and outcomes from, learning. Spe-cifically, these goals include students’ work avoidance and social achieve-ment goals (Ainley, 1993; Blumenfeld, 1992; Dowson & McInerney, 2001;McInerney et al., 1998; Nicholls & Utesch, 1998; Urdan & Maehr, 1995;Wentzel, 1994). As these “new” goals may also affect students’ learning andachievement, it would be advantageous to have an instrument availablewhich accurately measures these goals.
Third, although some instruments—for example, the Motivated Strat-egies for Learning Questionnaire (MSLQ) (Pintrich, Smith, Garcia, &McKeachie, 1991) and the Inventory of School Motivation (ISM)(McInerney & Sinclair, 1991; McInerney et al., 1998)—have attempted tomeasure various combinations of students’academic and social achievementgoals, as well as cognitive and metacognitive strategies, none have attemptedto measure these four sets of constructs in one instrument. Thus, a compre-hensive instrument measuring an identified range of students’goals and strat-egies is not yet available in the literature.
This is an important point because the absence of a comprehensive instru-ment designed to measure an identified range of goals and strategies mayforce researchers to use different instruments to assess constructs relevant totheir research. These scales, however, may have different psychometric prop-erties that are unknown until after the data have been gathered. The presentresearch, in contrast, specifically seeks to demonstrate the validity of multi-ple scales drawn from one instrument. As such, this instrument may provide amore coherent set of measures that are less likely to cause measurement diffi-culties when used alongside each other in research programs.
Fourth, recent research has emphasized that students can and do hold mul-tiple goals and strategies in school settings (Ainley, 1993; Derry, 1990;Meece & Holt, 1993; Pintrich & Shrauben, 1992; Seifert, 1995). Moreover,the way students organize and coordinate their multiple goals and strategiesis substantially related to their academic performance (Ainley, 1993;Dowson & McInerney, 1998; Meece, Blumenfeld, & Hoyle, 1988). Despite
DOWSON AND MCINERNEY 291
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this, the issue of how these goals and strategies may be structurally related toeach other has not been evaluated (for one recent exception, see McInerney,Marsh, & Yeung, in press).
This is particularly important because the literature relating to students’goals and strategies has consistently made the theoretical distinction betweenstudents’ academic and social goals (e.g., see Blumenfeld, 1992; Dowson &McInerney, 2001; Urdan & Maehr, 1995) and their cognitive andmetacognitive strategies (Barker, Dowson, & McInerney, in press; Bergin,1998; Biggs, 1987). But we are aware of no recent studies which haveattempted to verify (from a psychometric perspective) the distinctionbetween students’academic and social goals and between their cognitive andmetacognitive strategies. The present study, in contrast, explicitly seeks todetermine whether the conceptual distinction between these different classesof goals and strategies is, in fact, psychometrically supported.
Fifth, even where psychometric instruments exist that measure subsets ofstudents’ goals and strategies, their psychometric qualities are not alwaysdesirable. For example, the MSLQ, a widely used instrument for measuringstudents’ goals and strategies, has a goodness-of-fit index (GFI) of 0.77 forits items measuring motivational goals and a GFI of 0.78 for items measuringstudents’strategies (Pintrich et al., 1991). Moreover, factor loadings for someitems on their respective factors are as low as 0.17. There is the need, there-fore, for the development of an instrument that measures students’ goals andstrategies with enhanced validity.
Sixth, most instruments used for measuring students’ goals and/or strate-gies have been developed and validated with postsecondary students. Theseinclude the MSLQ, the Inventory of Learning Processes (ILP) (revised bySchmeck, Geisler-Brenstein, & Cercy, 1991), the Approaches to StudyInventory (ASI) (Entwistle & Ramsden, 1983), and the Strategic FlexibilityQuestionnaire (SFQ) (Cantwell, 1992). Few, if any, instruments in the litera-ture have been specifically developed with (and for use with) middle andsenior school students. The present instrument, however, has been specifi-cally designed with this target audience in mind.
Finally, most instruments measuring students’ motivational goals andstrategies have used items that were generated on the basis of a priori theoriz-ing concerning the content and structure of students’ goals and strategies.The instrument developed in this research, however, used items that werespecifically and intentionally developed from an inductive and qualitativeapproach to the content and structure of students’goals. Specifically, items inthe present instrument are grounded in the interview statements of studentsregarding their motivational goals and strategies. These interview statementswere generated in the context of a series of qualitative research projects con-ducted by present authors (i.e., Dowson & McInerney, 1997, 2001, in press).For this reason, the present instrument should display substantial content
292 EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT
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validity, which should manifest itself in enhanced measures of the instru-ments’ validity and reliability.
Gender Differences in Students’ Motivation and Cognition
Recent studies have begun to examine relations between students’genderand their goal orientations (e.g., Anderman & Young, 1994; Kaplan &Maehr, 1996; Midgley & Urdan, 1995). Studies have also investigated gen-der differences in patterns of students’ learning and achievement, and howthese may be related to students’differing motivational and strategic orienta-tions (e.g., Bouffard, Boisvert, Vezeau, & Larouche, 1995; Meece & Holt,1993; Wentzel, 1991). The literature, however, is not clear about how poten-tial gender differences may be related to students’motivation, cognition, andachievement (e.g., Ford, 1992; Meece & Jones, 1996; Midgley, Arunkumar,& Urdan, 1996). For these reasons, it is important to evaluate if the measure-ment of students’ goals and strategies is equally valid with women and men.If may be, for example, that women and men interpret items relating to goalsand/or strategies differently. This, in turn, may affect the measurement valid-ity of an instrument measuring these constructs.
Objectives
Given the above, the development and validation of a new instrument de-signed to measure an expanded range of students’ goals and strategies ap-pears to be warranted and necessary. The specific objectives of the presentstudy were the following:
• to describe the development of a new instrument designed to measure anidentified range of students’ academic and social goals, as well as students’cognitive and metacognitive strategies;
• to assess the psychometric properties of this instrument;• to evaluate if a multidimensional, hierarchical structure is appropriate for
measuring students’ goals and strategies; and• to determine whether the instrument is factorially invariant with women and
men.
Instrument Development
The Goal Orientations and Learning Strategies Survey (GOALS-S) wasdesigned to measure three academic goals, five social goals, three cognitivestrategies, and three metacognitive strategies. As indicated above, the moti-vational goals measured by the GOALS-S corresponded to those goals iden-tified in previous qualitative studies by the authors. Moreover, the items mea-suring these goals were based on the actual words of students’ in interview
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situations within these studies. Table 1 describes the constructs (goals) andthe items on the GOALS-S for the constructs.
The cognitive and metacognitive strategies measured by the GOALS-Scorrespond to the key strategies identified in previous studies (e.g., Biggs,1987; Derry, 1990; Pintrich et al., 1991; Schmeck et al., 1991). However, theactual items measuring these strategies in the GOALS-S were also generatedfrom students’ interview statements in the same qualitative research contextsas described above. Brief descriptions of these constructs (cognitive andmetacognitive strategies) and the items on the GOALS-S for these constructsare also presented in Table 1.
Method
Participants
Participants were 720 middle (n = 602) and senior (n = 118) school stu-dents from six high schools in Sydney, Australia. Of these students, 328(46%) were female and 392 (54%) were male, with the mean age of all stu-dents being 14.4 years. In addition, 598 (83%) of the students were fromAnglo-Australian backgrounds, with the rest being primarily AsianAustralians.
Procedures
Measures. The 84 items comprising the GOALS-S were initially reviewedby a sample of students (n = 8) and teachers (n = 2) for face validity of theitems. This involved students and teachers commenting on the wording of theitems with respect to their interpretability and coherence. Some items werereworded as a result of comments made by students and teachers regardingthe meaning of particular items. A 5-point Likert-type scale was constructedfor each item ranging from 1 (strongly disagree), 3 (not sure), to 5 (stronglyagree).
Administration. The GOALS-S was administered to participants in classgroups by the first author, with the assistance of teaching staff at each school.To standardize the delivery of the GOALS-S across class groups, teacherswho assisted in the administration of the GOALS-S received a copy of theinstrument, along with written instructions. The researchers also verballybriefed the participating teachers about the structure, purpose, and adminis-tration of the GOALS-S, prior to its administration with students. In particu-lar, teachers were instructed not to interpret any of the GOALS-S items forstudents, but to instruct students to leave an item out if they did not under-stand it.
294 EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT
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295
Tabl
e 1
Goa
ls a
nd S
trat
egie
s M
easu
red
by th
e G
oal O
rien
tati
on a
nd L
earn
ing
Stra
tegi
es S
urve
y (G
OA
LS-
S)
Con
stru
ct (
Goa
l or
Stra
tegy
)G
OA
LS-
S It
ems
Alp
ha
Aca
dem
ic g
oals
Mas
tery
: Wan
ting
to a
chie
ve to
dem
onst
rate
D2.
I w
ant t
o do
wel
l at s
choo
l to
show
that
I c
an le
arn
new
thin
gs.
.78
unde
rsta
ndin
g, a
cade
mic
com
pete
nce,
or
D5.
I w
ant t
o do
wel
l at s
choo
l to
show
that
I c
an le
arn
diff
icul
t sch
oolw
ork.
impr
oved
per
form
ance
rel
ativ
e to
D11
.I
try
hard
to u
nder
stan
d m
y sc
hool
wor
k.se
lf-e
stab
lishe
d st
anda
rds.
D14
.I
wor
k ha
rd to
und
erst
and
new
thin
gs a
t sch
ool.
D22
.I
wor
k ha
rd a
t sch
ool b
ecau
se I
am
inte
rest
ed in
wha
t I a
m le
arni
ng.
D24
.I
try
hard
at s
choo
l bec
ause
I a
m in
tere
sted
in m
y w
ork.
Perf
orm
ance
: Wan
ting
to a
chie
ve to
out
perf
orm
D3.
I w
ant t
o do
wel
l in
scho
ol b
ecau
se b
eing
bet
ter
than
oth
ers
is im
port
ant t
o m
e..8
7ot
her
stud
ents
, atta
in c
erta
in g
rade
s/m
arks
, or
D6.
I tr
y to
do
wel
l at s
choo
l bec
ause
I a
m o
nly
happ
y w
hen
I am
one
of
the
best
in th
e cl
ass.
obta
in ta
ngib
le r
ewar
ds a
ssoc
iate
d w
ithD
9.I
wan
t to
lear
n th
ings
so
that
I c
an c
ome
near
the
top
of th
e cl
ass.
acad
emic
per
form
ance
.D
12.
I w
ant t
o le
arn
thin
gs s
o th
at I
can
get
goo
d m
arks
.D
15.
Whe
n I
do g
ood
scho
olw
ork
it’s
beca
use
I am
tryi
ng to
be
bette
r th
an o
ther
s.D
18.
I w
ant t
o do
wel
l in
scho
ol s
o th
at I
am
one
of
the
best
in m
y cl
ass.
Wor
k av
oida
nce:
Wan
ting
to a
chie
ve w
ith a
s lit
tleD
7.I
choo
se e
asy
optio
ns in
sch
ool s
o th
at I
don
’t h
ave
to w
ork
too
hard
..7
2pe
rcei
ved
effo
rt a
s po
ssib
le.
D10
.A
t sch
ool I
wan
t to
do a
s lit
tle w
ork
as p
ossi
ble.
D13
.If
sch
oolw
ork
is to
o ha
rd f
or m
e I
just
don
’t d
o it.
D16
.I
don’
t ask
que
stio
ns in
sch
ool e
ven
whe
n I
don’
t und
erst
and
the
wor
k.D
19.
I do
n’t d
o sc
hool
wor
k if
it lo
oks
too
hard
to le
arn.
D23
.I
wan
t to
do w
ell a
t sch
ool,
but o
nly
if th
e w
ork
is e
asy.
Soci
al g
oals
Soci
al a
ffili
atio
n: W
antin
g to
ach
ieve
toC
1.I
wan
t to
do w
ell a
t sch
ool s
o th
at I
can
fee
l clo
se to
my
grou
p of
fri
ends
..8
3en
hanc
e a
sens
e of
bel
ongi
ng to
a g
roup
C6.
Whe
n I
wan
t to
do w
ell a
t sch
ool i
t’s s
o th
at I
can
hav
e a
lot o
f fr
iend
s.
by Ramona Palos on October 12, 2009 http://epm.sagepub.comDownloaded from
296
or g
roup
s an
d/or
to b
uild
or
mai
ntai
nC
11.
I tr
y to
und
erst
and
my
scho
olw
ork
so th
at I
will
fee
l par
t of
my
grou
p of
fri
ends
.in
terp
erso
nal r
elat
ions
hips
.C
16.
I tr
y to
do
wel
l at s
choo
l so
that
I w
on’t
fee
l lef
t out
if I
don
’t d
o w
ell.
C20
.I
do g
ood
scho
olw
ork
so th
at o
ther
peo
ple
will
wan
t to
be f
rien
ds w
ith m
e.C
26.
I do
my
best
at s
choo
l so
that
my
frie
nds
and
I w
ill b
e ab
le to
sta
y to
geth
er.
Soci
al a
ppro
val:
Wan
ting
to a
chie
ve to
gai
n th
eC
3.I
wan
t to
do w
ell a
t sch
ool s
o th
at I
can
get
pra
ise
from
my
teac
hers
..8
4ap
prov
al o
f pe
ers,
teac
hers
, and
/or
pare
nts.
C8.
I do
goo
d w
ork
at s
choo
l bec
ause
I w
ant t
o be
rec
ogni
zed
by m
y te
ache
rs.
C12
.I
wan
t to
get p
rais
e fr
om m
y te
ache
rs f
or g
ood
scho
olw
ork.
C17
.I
try
to d
o w
ell a
t sch
ool t
o pl
ease
my
teac
hers
.C
21.
I w
ant t
o do
wel
l in
my
scho
olw
ork
to p
leas
e m
y pa
rent
s.C
26.
I do
goo
d w
ork
at s
choo
l so
that
I c
an g
et p
rais
e fr
om m
y pa
rent
s.
Soci
al c
once
rn: W
antin
g to
ach
ieve
aca
dem
ical
lyC
2.I
try
to d
o w
ell a
t sch
ool s
o th
at I
can
I h
elp
my
frie
nds
with
thei
r sc
hool
wor
k.7
4to
be
able
to a
ssis
t oth
ers
in th
eir
acad
emic
whe
n th
ey n
eed
it.or
per
sona
l dev
elop
men
t.C
7.I
do m
y be
st a
t sch
ool s
o th
at I
can
giv
e m
y fr
iend
s he
lp w
ith th
eir
scho
olw
ork.
C14
.I
wan
t to
do w
ell a
t sch
ool s
o th
at I
can
hel
p ot
her
stud
ents
with
thei
r w
ork.
C19
.I
do g
ood
scho
olw
ork
so th
at I
can
hel
p ot
her
stud
ents
do
wel
l at s
choo
l.C
24.
I do
goo
d sc
hool
wor
k so
that
oth
er p
eopl
e ca
n le
arn
thin
gs f
rom
me
if th
ey a
sk.
C28
.W
hen
I w
ant t
o do
wel
l at s
choo
l it’s
so
that
I c
an h
elp
othe
r st
uden
ts.
Soci
al r
espo
nsib
ility
: Wan
ting
to a
chie
ve to
C5.
I w
ant t
o do
goo
d sc
hool
wor
k be
caus
e ot
her
peop
le e
xpec
t it o
f m
e..8
2m
aint
ain
inte
rper
sona
l com
mitm
ents
, mee
tC
10.
I w
ant t
o do
wel
l at s
choo
l to
show
that
I a
m b
eing
a r
espo
nsib
le s
tude
nt.
soci
al r
ole
oblig
atio
ns, o
r fo
llow
soc
ial a
ndC
15.
Whe
n I
do g
ood
scho
olw
ork
it’s
to s
how
that
I a
m b
eing
a r
espo
nsib
le s
tude
nt.
mor
al r
ules
.C
20.
I w
ant t
o do
wel
l at s
choo
l so
that
I d
on’t
get
in a
ny tr
oubl
e.C
25.
I av
oid
getti
ng in
to tr
oubl
e at
sch
ool b
y do
ing
good
sch
oolw
ork.
C30
.I
do g
ood
scho
olw
ork
so th
at I
don
’t h
ave
any
trou
ble
with
my
pare
nts
or te
ache
rs.
(con
tinu
ed)
by Ramona Palos on October 12, 2009 http://epm.sagepub.comDownloaded from
297
Tabl
e 1
(con
tinue
d)
Con
stru
ct (
Goa
l or
Stra
tegy
)G
OA
LS-
S It
ems
Alp
ha
Soci
al s
tatu
s: W
antin
g to
ach
ieve
to a
ttain
C4.
I do
goo
d sc
hool
wor
k so
that
I c
an g
et a
goo
d jo
b in
the
futu
re.
.84
wea
lth a
nd/o
r po
sitio
n in
sch
ool a
nd/o
r la
ter
life.
C9.
I tr
y to
do
wel
l at s
choo
l so
that
I c
an g
et a
goo
d jo
b w
hen
I le
ave
scho
ol.
C13
.I
do g
ood
scho
olw
ork
so th
at I
can
hav
e a
good
fut
ure.
C18
.I
do w
ell a
t sch
ool s
o th
at I
can
get
a h
igh-
payi
ng jo
b la
ter
on.
C22
.I
do m
y be
st in
sch
ool b
ecau
se I
am
tryi
ng to
hav
e a
good
fut
ure.
C27
.I
wan
t to
do w
ell a
t sch
ool s
o th
at I
can
hav
e lo
ts o
f m
oney
late
r on
.
Cog
nitiv
e st
rate
gies
Ela
bora
tion:
Mak
ing
conn
ectio
ns b
etw
een
B6.
Whe
n le
arni
ng th
ings
for
sch
ool,
I tr
y to
see
how
they
fit t
oget
her
with
oth
er th
ings
.73
pres
ent a
nd p
revi
ousl
y le
arne
d in
form
atio
n—th
isI
alre
ady
know
.m
ay in
volv
e pa
raph
rasi
ng, g
ener
atin
g an
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ning
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or s
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oth
eran
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ame
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imila
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ings
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and
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othe
r.B
28.
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and
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t I le
arn
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ted
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ther
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gs I
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w.
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y to
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ilari
ties
and
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eren
ces
betw
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gs I
am
lear
ning
for
sch
ool
and
thin
gs I
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w a
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dy.
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y to
mat
ch w
hat I
alr
eady
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w w
ith th
ings
I a
m tr
ying
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arn
for
scho
ol.
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aniz
atio
n: S
elec
ting,
seq
uenc
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out
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g,B
5.I
try
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rgan
ize
my
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ol n
otes
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t to
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ings
for
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.82
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r su
mm
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ing
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rtan
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orm
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n.B
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orga
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olw
ork
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at I
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erst
and
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tter.
B15
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nize
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t I h
ave
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r sc
hool
so
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nder
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r.B
17.
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p m
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arn
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olw
ork.
B23
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I w
ant t
o le
arn
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gs f
or s
choo
l, I
try
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rran
ge th
em s
o th
at I
can
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erst
and
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ter.
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n I
wan
t to
lear
n so
met
hing
for
sch
ool,
I m
ake
sure
that
I a
m o
rgan
ized
.
by Ramona Palos on October 12, 2009 http://epm.sagepub.comDownloaded from
298
Reh
ears
al: L
istin
g, m
emor
izin
g, r
eciti
ng, a
nd/o
rB
2.W
hen
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ant t
o le
arn
thin
gs f
or s
choo
l, I
prac
tice
repe
atin
g th
em to
mys
elf.
.76
nam
ing
fact
s/ite
ms
to b
e le
arne
d.B
8.W
hen
I w
ant t
o le
arn
thin
gs f
or s
choo
l, I
rere
ad m
y no
tes.
B14
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try
to m
emor
ize
thin
gs I
wan
t to
lear
n fo
r sc
hool
.B
20.
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emor
ize
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gs I
wan
t to
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n fo
r sc
hool
.B
26.
I re
peat
thin
gs to
mys
elf
whe
n le
arni
ng th
ings
for
sch
ool.
B32
.I
rere
ad m
y bo
oks
whe
n I
wan
t to
lear
n th
ings
for
sch
ool.
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a-co
gniti
ve s
trat
egie
sM
onito
ring
: Inv
olve
s se
lf-c
heck
ing
for
B12
.I
ofte
n as
k m
ysel
f qu
estio
ns to
see
if I
und
erst
and
wha
t I a
m le
arni
ng.
.83
unde
rsta
ndin
g, s
elf-
test
ing,
and
org
aniz
ing
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y to
dec
ide
wha
t par
ts o
f m
y sc
hool
wor
k I
don’
t kno
w a
s w
ell a
s ot
hers
.re
view
s of
lear
ned
mat
eria
l—im
plie
s sy
stem
atic
E10
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ofte
n ch
eck
to s
ee if
I u
nder
stan
d w
hat I
hav
e re
ad.
atte
mpt
to e
valu
ate
the
assi
mila
tion
and
E30
.I
ofte
n tr
y to
dec
ide
wha
t par
ts o
f m
y sc
hool
wor
k I
don’
t kno
w w
ell.
orga
niza
tion
of le
arne
d m
ater
ial.
E36
.I
chec
k to
see
if I
und
erst
and
the
thin
gs I
am
tryi
ng to
lear
n.E
42.
I tr
y to
mak
e su
re th
at I
und
erst
and
wha
t I a
m le
arni
ng.
Plan
ning
: Inv
olve
s pr
iori
tizin
g, ti
me
man
agem
ent,
E4.
I of
ten
look
thro
ugh
book
s to
see
how
they
are
arr
ange
d be
fore
I s
tart
rea
ding
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1sc
hedu
ling,
set
ting
real
istic
goa
ls, a
nd a
rran
ging
E5.
Whe
n I
wan
t to
lear
n th
ings
for
sch
ool I
pic
k ou
t the
mos
t im
port
ant p
arts
firs
t.w
ork
envi
ronm
ents
app
ropr
iate
ly—
impl
ies
E7.
Bef
ore
tryi
ng to
lear
n th
ings
for
sch
ool I
try
to d
ecid
e w
hat t
he m
ost i
mpo
rtan
t par
ts o
fth
ough
tful
pre
para
tion
for
com
plet
ing
wor
k.w
hat I
am
tryi
ng to
lear
n ar
e.E
9.I
ofte
n pl
an a
head
so
that
I c
an d
o w
ell i
n m
y sc
hool
wor
k.E
11.
I of
ten
try
to d
ecid
e fir
st w
hat a
re th
e m
ost i
mpo
rtan
t par
ts o
f w
hat I
hav
e to
lear
nfo
r sc
hool
.E
14.
I tr
y to
pla
n ou
t my
scho
olw
ork
as b
est I
can
.
Reg
ulat
ing:
The
str
ateg
ies
put i
n pl
ace
to r
ectif
yE
13.
If I
don
’t u
nder
stan
d m
y sc
hool
wor
k, I
ask
the
teac
her
to h
elp
me.
.79
defi
cits
iden
tifie
d w
hile
mon
itori
ng—
spec
ific
E18
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I a
m h
avin
g tr
oubl
e le
arni
ng s
omet
hing
at s
choo
l, I
ask
for
help
.st
rate
gies
incl
ude
atte
mpt
ing
diff
eren
t way
s to
E25
.W
hen
I do
n’t u
nder
stan
d so
met
hing
at s
choo
l, I
try
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et s
omeo
ne to
hel
p m
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arn
mat
eria
l, se
ekin
g ex
plan
atio
ns f
rom
E34
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I g
et c
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t som
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t sch
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nd id
entif
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rea
soni
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t sch
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ater
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40.
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don
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nder
stan
d so
met
hing
in s
choo
l, I
go b
ack
and
try
to le
arn
it ag
ain.
by Ramona Palos on October 12, 2009 http://epm.sagepub.comDownloaded from
Confirmatory Factor Analysis (CFA)
CFAs assess the extent to which the observed indicators (items) reflect thestructure of the underlying constructs. CFAs allow the researcher to specifynot only how many factors are measured by a given set of items but, also,which items function as indicators of which factors (Fleishman & Benson,1987).
Model fit is assessed by (a) model parameter estimates and (b) a combina-tion of model fit indices. In this study, chi-square statistic and several descrip-tive fit indices were used, including the Tucker-Lewis Index (TLI), the Parsi-mony Relative Noncentrality Index (PRNI), the root mean square error ofapproximation (RMSEA), and the chi-square/degrees of freedom ration.
It is generally accepted that, in good measurement models, the TLI andPRNI will be greater than 0.90 and the RMSEA will be less that 0.05. How-ever, it should be noted that a TLI and/or PRNI of 0.90 (or greater) may notdirectly correspond to an RMSEA of .05 (or less) (see Hu & Bentler, 1999).For this reason, care should be exercised when interpreting models wherediscrepancies between the accepted values for the TLI, PRNI, and RMSEAdo not directly correspond.
Higher Order CFAs (HCFAs)
First-order CFAs seek to ascertain whether various combinations of itemsmay measure the same underlying construct or factor. In a similar way,HCFAs seek to ascertain whether various combinations of first-order factorsmay measure higher order factors. There are two distinct advantages in iden-tifying higher order factors, if they exist. The first is that models may be sim-plified by their inclusion, that is, a smaller number of higher order factorsmay be shown to account for variations in and between individual items andfirst-order factors (Lance, Teachout, & Donnelly, 1992). The second is thatthe inclusion of higher order factors enables researchers to identify hierarchi-cal relations between first-order factors (Marsh & Hocevar, 1985). If thesehierarchical relations conform to relations predicted from theory, the theoret-ical substance of models is enhanced. One distinct disadvantage, however, ofmodels incorporating higher order factors is that they may explain less vari-ance in the data than first-order models. A criterion for evaluating the useful-ness of higher order models, then, is the extent to which the advantagesgained from model simplification are balanced by the losses incurred in theexplanatory power of these models (Lance et al., 1992).
The HCFAs reported here hypothesized that:
• three academic goals (mastery, performance, and work avoidance) wouldreflect a second-order factor, academic goals;
DOWSON AND MCINERNEY 299
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• five social goals (social affiliation, approval, conformity, responsibility,present and future status, and concern) would reflect the second second-order factor, social goals;
• three cognitive strategy factors would reflect the second-order factor cogni-tive strategies; and
• three metacognitive strategy factors would reflect the second-order factormetacognitive strategies.
Assessing Factorial Invariance
Invariance analysis provides information about the equivalence of datastructure across multiple groups (Marsh, 1993, 1994; Marsh & Hocevar,1985). Different degrees of invariance may be assessed. The present investi-gation evaluates the invariance of factor structures between men and womento see if these structures are invariant in terms of factor pattern matrix acrossgender groups.
CFA Procedures
All cases exhibiting missing data were removed for CFA analyses. Thisleft 702 cases available for analysis. It should be noted that (a) listwise dele-tion of cases may cause biases in parameter estimates and reliability esti-mates, and (b) other methods for dealing with missing data (such as maxi-mum likelihood procedures) are available (Ding, Velicer, & Harlow, 1995).Despite this, listwise deletion of cases is still widely accepted as an appropri-ate and rigorous procedure for dealing with missing data (Bollen, 1989;Byrne, 1998; Mueller, 1996).
Following procedures used by McInerney, Marsh, and McInerney (1999),separate CFAs were used to assess conceptually distinct sets of scales relat-ing to students’goal orientations and the scales relating to students’cognitiveand meta-cognitive strategy use. All items were specified as indicators ofonly one factor, and the uniqueness of each item was modeled to be inde-pendent. The factor correlations (correlations between the eight goal orienta-tion and six strategy scales) were allowed to freely associate with each other.
All analyses were conducted using LISREL 7, and all parameters wereestimated using the maximum likelihood procedure. An underlying assump-tion of maximum likelihood estimation procedures is that responses are nor-mally distributed (Hu, Bentler, & Kano, 1992). As is common inpsychometric research, however, responses to the GOALS-S were not nor-mally distributed. (In general, responses to the GOALS-S were negativelyskewed and moderately leptokurtic.) Fortunately, however, maximum likeli-hood estimation procedures appear to be robust with respect to violations ofnormality, particularly in relation to parameter estimates and goodness-of-fitindices (Hu et al., 1992; Joreskog & Sorbom, 1993; Muthen & Kaplan,
300 EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT
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1985). In fact, to the extent that estimation problems are associated withnonnormality, parameter estimates and observed goodness-of-fit measurestend to indicate a poorer fit if data are nonnormally distributed (Hau &Marsh, 2000). For this reason, nonnormality does not appear to be a signifi-cant problem with respect to maximum likelihood estimation procedures.
Results
Models for Goal Orientation Scales
The results for the initial goal orientation model (M1) indicate that thismodel fitted the data only marginally well. The chi-square/degrees of free-dom ratio for M1 is greater than 2, the TLI is less than 0.9, and the RMSEA isonly marginally less than 0.05. The PRNI, however, is greater than 0.9, andthe solution as a whole was proper (i.e., no negative factor variances or otherimpossible parameters were identified).
Closer inspection of the factor loadings, uniquenesses, and modificationindices (indices which measure the extent to which items load on factorsother than the factor on which they were hypothesized to load) associatedwith the estimated model (M1) indicated that several items in the hypothe-sized model fit the data poorly. These 12 items displayed factor pattern coef-ficients less than 0.5, uniquenesses greater that 0.7, and maximum modifica-tion indices greater than 20.0. These items were removed from theirrespective scales.
Once the 12 poorly fitting items were removed, the new goal orientationmodel (model for best 36 items, or M2) was evaluated. This model showed agood fit with the data. The chi-square/degrees of freedom ration is less than 2,the TLI and PRNI are both greater than 0.9, and the RMSEA is substantiallyless than 0.05. Thus, removing the poorly fitting items from the originalmodel substantially improved the models overall fit with the data.
Models for Cognitive andMetacognitive Strategy Scales
The results for the initial strategy model (M3) showed that this model fitthe data reasonably well. The chi-square/degrees of freedom ratio for M6 isgreater than 2, but not substantially so, the PRNI is greater than 0.9, theRMSEA is less than 0.05, and the solution as a whole was proper. However,the TLI was less than 0.90.
Inspection of the factor pattern coefficients, uniquenesses, and modifica-tion indices associated with M3 again indicated that several items in thehypothesized model fit the data poorly. These 8 items displayed factor pat-
DOWSON AND MCINERNEY 301
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302
Tabl
e 2
Mod
el F
it S
tati
stic
s fo
r G
oal O
rien
tati
on a
nd L
earn
ing
Stra
tegi
es S
urve
y (G
OA
LS-
S) S
cale
s
Mod
elχ2
dfχ2 /d
fT
LI
PRN
IR
MSE
AM
odel
Des
crip
tion
Mod
els
for
goal
ori
enta
tion
scal
esM
12,
777.
281,
052
2.64
.864
.916
.048
Hyp
othe
size
d m
odel
M2
1,00
7.48
566
1.78
.908
.962
.041
Mod
el f
or b
est 3
6 ite
ms
Mod
els
for
cogn
itive
and
met
acog
nitiv
est
rate
gy s
cale
sM
31,
277.
5957
92.
21.8
81.9
37.0
45H
ypot
hesi
zed
mod
elM
445
5.6
335
1.36
.923
.981
.039
Mod
el f
or b
est 2
8 ite
ms
Hig
her
orde
r m
odel
sM
51,
030.
4555
71.
85.9
04.9
59.0
42G
oal o
rien
tatio
ns (
36 it
ems,
2 h
ighe
r or
der
fact
ors)
M6
488.
7232
81.
49.9
16.9
80.0
38St
rate
gies
(28
item
s, 2
hig
her
orde
r fa
ctor
s)Te
sts
of in
vari
ance
for
hig
her
orde
r m
odel
s(i
nvar
iant
fac
tor
mat
rix)
M7
875.
5852
11.
68.9
13.9
70.0
39G
oal o
rien
tatio
ns (
wom
en)
M8
998.
6352
11.
92.9
01.9
59.0
42G
oal o
rien
tatio
ns (
men
)M
942
1.50
300
1.41
.920
.981
.038
Stra
tegi
es (
wom
en)
M10
462.
3830
01.
54.9
13.9
75.0
39St
rate
gies
(m
en)
Not
e. T
LI
= T
ucke
r-L
ewis
Ind
ex; P
RN
I =
Par
sim
ony
Rel
ativ
e N
once
ntra
lity
Inde
x; R
MSE
A =
roo
t mea
n sq
uare
err
or o
f ap
prox
imat
ion.
by Ramona Palos on October 12, 2009 http://epm.sagepub.comDownloaded from
tern coefficients less than 0.5, uniquenesses greater that 0.7, and maximummodification indices greater than 20.0, and were removed from their respec-tive scales.
Once the 8 poorly fitting items were removed, the new strategy model(best 28 items, or M3) was evaluated. This model showed a good fit with thedata. The chi-square/degrees of freedom ratio is less than 2, the TLI andPRNI are both greater than 0.9, and the RMSEA is substantially less than0.05. Thus, removing the poorly fitting items from the original model sub-stantially improved the model’s overall fit with the data.
Models for Higher Order Factors
Results of the HCFAs (Models M5 and M6) indicated that the higher ordermodels for goal orientations and strategies fit the data well. Both solutionswere proper, and all indices fell within the range indicating good fit. Theseresults support the contention that a hierarchical structure of goals and strate-gies is indicated by the present data. Moreover, as both higher order modelsfit the data nearly as well as their corresponding first-order models, they maybe accepted as a more parsimonious account of the data.
Test of Model Invariance
Given that the higher order models fit the data nearly as well as the first-order models, these were used in testing for invariance between men andwomen. The tests of invariance for the goal orientation and strategy higherorder models constrained the factor pattern coefficients in these models to beinvariant across groups. The tests of invariance for women (Models M7 andM8) and men (Models M9 and M10) all showed good fit with the data, withall indices falling within acceptable ranges. This indicates that the higherorder models for the goal orientation and strategies can be considered invari-ant across gender groups. However, in both cases the models for men fit thedata less well than the models for women. In particular, the TLI for the malegoal orientation model (M8) is only marginally above 0.9. Nevertheless, theoverall picture is that the factor structure of the higher order models, with theconstraint of the factor pattern matrix being invariant, is consistent acrossgroups.
Tables 3 and 4 present the factor pattern and structure matrices, as well asthe interfactor correlations for the goal orientation and cognitive strategyscales. Table 5 presents the second-order factor loadings and correlations forthe higher order factor models.
DOWSON AND MCINERNEY 303
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304 EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT
Table 3Factor Pattern and Structure Coefficients and Factor Correlations for Goal Orientation andLearning Strategies Survey (GOALS-S) Orientation Scales
Work Social Social Social Social SocialMastery Performance Avoidance Affiliation Approval Responsibility Status Concern
D2 .84 .28 .22 .13 .12 .08 .12 .09D11 .80 .29 .23 .14 .13 .09 .13 .10D14 .80 .29 .23 .13 .14 .10 .13 .11D22 .79 .31 .25 .15 .15 .11 .14 .12D24 .80 .29 .23 .14 .13 .10 .13 .10D3 .27 .86 .19 .09 .10 .12 .08 .07D6 .30 .76 .22 .12 .12 .14 .10 .10D9 .29 .80 .20 .11 .11 .13 .09 .08D12 .30 .78 .21 .11 .11 .14 .10 .08D15 .29 .78 .20 .12 .12 .14 .10 .09D7 .21 .17 .87 .09 .10 .08 .07 .12D13 .25 .21 .76 .12 .12 .12 .09 .14D16 .26 .22 .74 .12 .13 .12 .10 .15D19 .25 .21 .77 .11 .12 .11 .09 .14D23 .22 .19 .82 .10 .10 .09 .08 .13C1 .14 .12 .14 .80 .29 .32 .23 .37C6 .13 .10 .12 .86 .27 .30 .21 .33C11 .13 .11 .12 .84 .26 .31 .21 .34C16 .15 .12 .15 .79 .30 .29 .24 .37C3 .14 .10 .10 .27 .84 .33 .27 .30C8 .14 .11 .11 .30 .81 .34 .27 .31C21 .13 .10 .09 .26 .86 .32 .26 .29C26 .15 .12 .11 .29 .79 .36 .28 .33C5 .10 .14 .10 .33 .35 .80 .20 .36C10 .09 .13 .09 .32 .34 .88 .19 .35C15 .11 .14 .10 .33 .34 .83 .20 .36C25 .13 .16 .10 .35 .37 .76 .21 .39C4 .14 .11 .10 .24 .28 .22 .81 .20C13 .15 .12 .12 .25 .29 .23 .79 .21C18 .12 .09 .09 .21 .25 .19 .87 .18C22 .11 .09 .09 .21 .25 .19 .90 .17C27 .15 .11 .12 .24 .29 .22 .80 .20C14 .12 .10 .15 .38 .33 .39 .20 .77C19 .10 .08 .12 .35 .30 .36 .18 .85C24 .10 .09 .13 .37 .31 .37 .19 .82C28 .09 .07 .12 .33 .29 .34 .17 .88Factor correlation (phi) matrix
Mastery 1.00Performance –.37 1.00Work Avoidance .29 –.25 1.00Social Affiliation .17 .13 .14 1.00Social Approval .18 .14 .13 .34 1.00Social Responsibility .11 .18 .11 .40 .42 1.00Social Status .17 .12 .11 .27 .32 .24 1.00Social Concern .12 .09 .16 .43 .38 .44 .22 1.00
Note. Italicized numbers are the factor pattern coefficients (i.e., the factor loadings) for each item with its des-ignated factor. Nonitalicized numbers are the factor structure coefficients (i.e., the correlations) of each itemwith its nondesignated factors. For the present model, the factor pattern and factor structure coefficients areequal for the items with their designated factors.
by Ramona Palos on October 12, 2009 http://epm.sagepub.comDownloaded from
Discussion
Several important features of the GOALS-S emerge from the resultsreported above. First, the analyses support the factorial validity of the first-order structure of the GOALS-S. This finding supported the hypothesized
DOWSON AND MCINERNEY 305
Table 4Factor Loadings, Item-Factor Correlations, and Factor Correlations for Goal Orientationand Learning Strategies Survey (GOALS-S) Cognitive and Metacognitive Strategy Scales
Rehearsal Elaboration Organization Planning Monitoring Regulating
B2 .83 .43 .37 .23 .24 .22B8 .82 .43 .37 .24 .25 .21B14 .90 .40 .35 .22 .23 .19B20 .84 .42 .36 .23 .24 .21B26 .78 .44 .39 .25 .26 .24B16 .42 .81 .44 .17 .12 .11B22 .43 .81 .43 .18 .11 .11B28 .43 .80 .44 .18 .12 .12E2 .38 .92 .40 .15 .09 .09E3 .43 .82 .43 .17 .12 .11B10 .36 .35 .90 .25 .24 .32B17 .37 .38 .87 .26 .25 .33B23 .39 .39 .83 .27 .26 .34E6 .36 .35 .90 .25 .24 .32E4 .26 .18 .26 .80 .40 .39E5 .25 .18 .26 .83 .40 .38E7 .23 .17 .25 .90 .38 .37E9 .20 .16 .24 .95 .35 .36E11 .27 .19 .27 .79 .42 .41E8 .23 .09 .23 .35 .89 .35E10 .24 .10 .23 .36 .87 .36E30 .27 .11 .26 .44 .75 .42E36 .24 .09 .22 .36 .88 .36E13 .19 .09 .30 .36 .38 .88E18 .20 .11 .32 .37 .39 .83E34 .21 .12 .33 .38 .41 .80E37 .20 .13 .32 .36 .39 .84E40 .20 .12 .31 .37 .40 .82Factor correlation (phi) matrix
Rehearsal 1.00Elaboration .52 1.00Organization .43 .54 1.00Planning .28 .20 .30 1.00Monitoring .30 .12 .29 .50 1.00Regulating .24 .14 .38 .45 .47 1.00
Note. Italicized numbers are the factor pattern coefficients (i.e., the factor loadings) for each item with its des-ignated factor. Nonitalicized numbers are the factor structure coefficients (i.e., the correlations) of each itemwith its nondesignated factors. For the present model, the factor pattern and factor structure coefficients areequal for the items with their designated factors.
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factor structure for students’ academic and social achievement goals andtheir cognitive and metacognitive strategies. Moreover, the overall GOALS-S model fit is substantially better than some other instruments extant in theliterature (as reviewed earlier in this article). Both points are importantbecause a key objective of the present study was to develop a single instru-ment capable of measuring this range of constructs and to determine whetherthis instrument measured these constructs better than existing instruments.
Second, the results supported the second-order model structure of theGOALS-S. This finding is important because it showed that students’ goalsand strategies are multidimensional and hierarchical in structure, and theconceptual distinction between students’ goals (academic and social) andtheir strategies (cognitive and meta-cognitive) is supported.
Given this, the GOALS-S may provide a means by which researchers canfurther investigate students’ multiple goals and strategies and the ways thesemay interact to influence students’ motivation, cognition, and achievement.The hierarchical structure of the GOALS-S may also provide researcherswith a means of constructing more parsimonious models of student motiva-tion and cognition through the use of fewer higher order latent factors thatsubsume individual goals and strategies at the first-order level.
Third, the results support the factorial invariance of the second-ordermodels across gender groups. This finding is important because it addresses
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Table 5Factor Pattern Coefficients for Goal Orientation and Learning Strategies Survey (GOALS-S)Higher Order Models
Second-Order First-Order Factor Pattern Squared FactorFactor Factor Coefficients Pattern Coefficients
Goal orientations higherorder model (M9)
Academic goals Mastery .83 .68Performance .84 .71Work avoidance .81 .65
Social goals Affiliation .75 .57Approval .79 .62Responsibility .73 .53Status .78 .61Concern .66 .44
Cognitive strategies Rehearsal .83 .69Elaboration .76 .58Organization .69 .47
Metacognitive strategies Planning .79 .62Monitoring .85 .73Regulating .89 .79
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the concern that women and men may respond differently to items/scales thatmeasure their achievement goals and strategies.
Finally, the findings from the present sample provides support that theGOALS-S is a psychometrically sound instrument for use with middle andsenior school students. This is important because, as indicated previously,other instruments measuring students’goals and strategies have largely beendeveloped with postsecondary students. Thus, these instruments may not besuitable for use with high school students. Future research will be necessary,however, to evaluate the generalizability of the findings when the instrumentis used in samples of different populations.
Limitations of the Study
The primary limitation of the present study is that the modified first-ordermodels (M2 and M4) and second-order models (M5 and M6) were not evalu-ated by using independent samples. When model modifications are made onthe basis of result of initial CFAs, it is often necessary to assess the validity ofthese modified models with new data. Despite this, testing modified modelswith current data is an acceptable, if not ideal, procedure (Marsh, 1993;Marsh & Hocevar, 1985; McInerney et al., 1999). This acceptability is pri-marily generated by the practical difficulties involved if new data sets need tobe collected for every new model that is to be tested (Hayduk, 1987; Mueller,1996). Nevertheless, a clear direction for future research will be to evaluatethe modified models in other comparable samples.
Conclusion
The present research provides support for the GOALS-S as apsychometrically sound measure of middle and senior school students’ aca-demic and social goal orientations and their cognitive and metacognitivestrategies. Moreover, in doing so, the present study also provides support forthe multidimensionality and hierarchical structure of students’ goals andstrategies. Finally, the present study provides support for the factorialinvariance of the GOALS-S across gender groups. For these reasons, thepresent research makes a useful and necessary contribution measurement ofhigh school students’ motivational and cognitive processes.
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